Movie Database

# Load packages
# Data visualization
library(ggplot2)
library(lubridate)
library(dplyr)
library(stringr)

# Import data
movies <- read.csv('data/movies-clean.csv')

# Change datatypes
movies$release_date <- as_date(movies$release_date)

Question 1: How have movie genres changed over time?

Question 2: How do the attributes differ between Universal Pictures and Paramount Pictures?

Check: - number of films - popularity - runtime - genre_one - Vote_average - budget_adj - revenue_adj

Number of Films

Popularity

Runtime

Genre

Vote Average

Budget

Revenue

Question 3: How have movies based on novels performed relative to movies not based on novels?

Performance Metrics to look at: - popularity - runtime - vote_average - budget_adj - revenue_adj

Popularity

Runtime

Vote Average

Budget

Revenue

Question 4: Do budget and revenue, and budget and popularity have any correlation?

# Plot budget and revenue
ggplot(data=movies, aes(x=budget_adj, y=revenue_adj)) +
  geom_point() +
  geom_smooth(method='lm')
# Plot budget and popularity
ggplot(data=movies, aes(x=budget_adj, y=popularity)) +
  geom_point() +
  geom_smooth(method='lm')

# Plot revenue and popularity
ggplot(data=movies, aes(x=revenue, y=popularity)) +
  geom_point() +
  geom_smooth(method='lm')